🧠 Train a 64M-parameter LLM from scratch in just 2h!

TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.

A smarter, self-hosted AI assistant — multi-user, multi-agent.

Removes 20+ patterns of AI slop from any piece of writing.

Per-turn model & reasoning routing for Codex, driven by Jev (TypeSafe System One): picks the model, thinking depth and speed mode for every turn.

Ask your house a question, get a number back. Home Assistant integration for TypeSafe Jev: typed answers as sensors, four actions for automations, and a conversation agent for Assist.

HK is a unified neural model format and framework built in native Zig, designed to replace SafeTensors, GGUF, and PyTorch checkpoints. Features zero-copy mmap loading, dual-mode quantization, 2:4 hardware sparsity, live model expansion, and 137+ architecture support with bindings for Python, Rust, Go, C#, Java, and TypeScript.

A 777M-parameter Mixture-of-Experts language model trained from scratch for $55. Multi-head Latent Attention, DeepSeekMoE, own tokenizer.

Logic-based multi-LLM voice router for Home Assistant, combining native intents, local Qwen, GPU-aware desktop Ollama, and cloud AI with intelligent fallback.

Local, typed AI decisions for C, C++, C#, Python, Unity and Unreal Engine.

Who Let the Agents Act? is an interactive agentic-security lab exploring how AI agents use tools, access data, and make decisions. Compare vulnerable, prompt-only, and hardened agents to see why prompts are not security boundaries and how authorization, least privilege, data projection, output controls, etc. keep agents safe.